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Joint modeling of user and item preferences with interaction frequency and attention for knowledge graph-based recommendation

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Abstract

In recent years, knowledge graphs (KGs) have attracted considerable attention in recommendation research based on auxiliary information. The rich semantic knowledge in a KG can enrich user and item representations and provide more accurate recommendations. Unfortunately, most previous studies failed to incorporate user/item interaction frequency, which plays a critical role in analyzing users’ historical behaviors, into their recommendation models. Furthermore, the importance levels of users’ historical preference features and users’ KG preference features for modeling user preferences are different. Therefore, we propose an approach for jointly modeling the preferences of users and items with interaction frequency and attention (JMPIA), which first leverages an attention network with interaction frequency to obtain users’ and items’ historical preference representations and conducts preference propagation in a KG to obtain users’ KG preference representations for each hop. Then, we leverage an attention aggregator with the ReLU activation function to aggregate these representations to capture more accurate user preferences, thereby promoting recommendation. Finally, we conducted a comprehensive performance evaluation on two real-world datasets. The experimental results obtained on these two datasets demonstrate that the proposed JMPIA approach outperforms the state-of-the-art KG-based methods. These results validate the effectiveness of using an attention network with interaction frequency to derive preferences from users’ historical interaction information and combining them with the rich information in a KG to integrate user preferences.

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Data Availability

The datasets are available in https://grouplens.org/datasets/movielens/1m/ and https://grouplens.org/datasets/book-crossing/

Code Availability

The code for this article has been uploaded to Github: https://github.com/Jiahao-Liu121/JMPIA

References

  1. Guo Q, Zhuang F, Qin C, Zhu H, Xie X, Xiong H, He Q (2020) A survey on knowledge graph-based recommender systems. IEEE Trans Knowl Data Eng 34(8):3549–3568

  2. Liu Y, Jun M (2023) Knowledge-aware attentional neural network for review-based movie recommendation with explanations. Neural Comput Appl 35(3):2717–2735

    Article  Google Scholar 

  3. Assuncao WG, Piccolo LSG, Zaina LAM (2022) Considering emotions and contextual factors in music recommendation: A systematic literature review. Multimedia Tools Appl 81:8367–8407

    Article  Google Scholar 

  4. Wu C, Wu F, Huang Y, Xie X (2022) Personalized news recommendation: Methods and challenges. ACM Trans Inf Syst (TOIS) 1–49

  5. Da’u A, Salim N, Idris R (2021) An adaptive deep learning method for item recommendation system. Knowl Based Syst 213:1–12

    Article  Google Scholar 

  6. Wang M, Qiu L, Wang X (2021) A survey on knowledge graph embeddings for link prediction. Symmetry 13(3):1–29

    Article  Google Scholar 

  7. Zou X (2020) A survey on application of knowledge graph. J Phys Conference Series 1487(1):1–12

    Article  MathSciNet  Google Scholar 

  8. Wang X, Bo D, Shi C, Fan S (2022) A survey on heterogeneous graph embedding: Methods, techniques, applications and sources. IEEE Trans Big Data 9(2):415–436

    Article  Google Scholar 

  9. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI conference on artificial intelligence, pp 2181–2187

  10. Zhao Y (2022) Time-aware path reasoning on knowledge graph for recommendation. ACM Trans Inf Syst 41(2):1–26

    MathSciNet  Google Scholar 

  11. Liang X (2022) Meta-path-based heterogeneous graph neural networks in academic network. Int J Mach Learn Cybernetics 13:1553–1569

  12. Chen J, Gong Z, Li Y (2022) Meta-path based neighbors for behavioral target generalization in sequential recommendation. IEEE Trans Netw Sci Eng 9(3):1658–1667

    Article  Google Scholar 

  13. Yang Z (2023) Collaborative meta-path modeling for explainable recommendation. IEEE Trans Comput Social Syst 1–11

  14. Wang H, Zhao M, Xie X, Li W, Guo M (2019) Knowledge graph convolutional networks for recommender systems. In: Proceedings of the world wide web conference, pp 3307–3313

  15. Li Q, Tang X, Wang T, Yang H, Song H (2019) Unifying task-oriented knowledge graph learning and recommendation. IEEE Access 7:115816–115828

    Article  Google Scholar 

  16. Sha X, Sun Z, Zhang J (2021) Hierarchical attentive knowledge graph embedding for personalized recommendation. Electron Commerce Res Appl 48:1–40

    Article  Google Scholar 

  17. Wang Z, Li X, Yu Z, Guo B, Chen L, Zhou X (2022) Exploring multi-dimension user-item interactions with attentional knowledge graph neural networks for recommendation. IEEE Trans. Big Data 9(1):212–226

  18. Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 417–426

  19. Luo Y, Sha B, Xu T (2021) A recommended method based on the weighted RippleNet network mode. J Phys Conference Series 2025:1–10

    Article  Google Scholar 

  20. Wang Z, Lin G, Tan H, Chen Q, Liu X (2020) CKAN: Collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of the 43th international ACM SIGIR conference on research and development in information retrieval, pp 219–228

  21. Wang X, He X, Cao Y, Liu M, Chua T (2019) KGAT: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 950–958

  22. Xu Z, Liu H, Zhang Q (2022) CKGAT: Collaborative knowledge-aware graph attention network for top-n recommendation. Appl Sci 12(3):1–23

  23. Hui B, Zhang L, Zhou X, Wen X, Nian Y (2022) Personalized recommendation system based on knowledge embedding and historical behavior. Appl Intell 52(1):954–966

    Article  Google Scholar 

  24. Yin F, Ji M, Wang Y, Yao Z, Feng X, Li S (2022) Enhanced graph recommendation with heterogeneous auxiliary information. Complex Intell Syst 8(3):2311–2324

    Article  Google Scholar 

  25. Jiang W, Sun Y (2022) Social-RippleNet: Jointly modeling of ripple net and social information for recommendation. Appl Intell 53:3472–3487

  26. Zhang D, Wang H, Yang X, Ma Y (2023) Deep interest network based on knowledge graph embedding. Appl Sci 13(1):1–13

    Google Scholar 

  27. Duan H, Liu P, Ding Q (2023) RFAN: Relation-fused multi-head attention network for knowledge graph enhanced recommendation. Appl Intell 53(1):1068–1083

    Article  Google Scholar 

  28. Zhang F, Yuan N J, Lian D, Xie X, Ma W (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22th ACM SIGKDD international conference on knowledge discovery and data mining, pp 353–362

  29. Cao Y, Wang X, He X, Hu Z, Chua T (2019) Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In: Proceedings of the world wide web conference, pp 151–161

  30. Wang H, Zhang F, Xie X, Guo M (2018) DKN: Deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference, pp 1835–1844

  31. Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 33(12):6999–7019

  32. Ji G, He S, Xu L, Liu K, Zhao J (2015) Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53th annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, pp 687–696

  33. Dadoun A, Troncy R, Ratier O, Petitti R (2019) Location embeddings for next trip recommendation. In: Proceedings of the 2019 world wide web conference, pp 896–903

  34. Xin X, He X, Zhang Y, Jose J (2019) Relational collaborative filtering: Modeling multiple item relations for recommendation. In: Proceedings of the 42th international ACM SIGIR conference on research and development in information retrieval, pp 125–134

  35. Wang H, Zhang F, Hou M, Xie X, Guo M, Liu Q (2018) SHINE: Signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the 11th ACM international conference on web search and data mining, pp 592–600

  36. Yang Y, Zhu Y, Li Y (2022) Personalized recommendation with knowledge graph via dual-autoencoder. Appl Intell 52(6):6196–6207

    Article  Google Scholar 

  37. Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: A heterogeneous information network approach. In: Proceedings of the 7th ACM international conference on web search and data mining, pp 283–292

  38. Wang X, Wang D, Xu C, He X, Cao Y, Chua T (2019) Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the AAAI conference on artificial intelligence pp 5329–5336

  39. Ma W, Zhang M, Cao Y, Jin W, Wang C, Liu Y, Ma S, Ren X (2019) Jointly learning explainable rules for recommendation with knowledge graph. In: Proceedings of the world wide web conference, pp 1210–1221

  40. Tao S, Qiu R (2021) Multi-modal knowledge-aware reinforcement learning network for explainable recommendation. Knowl Based Syst 227:1–11

    Article  Google Scholar 

  41. Tao S, Qiu R (2021) Micro-behaviour with reinforcement knowledge-aware reasoning for explainable recommendation. Knowl Based Syst 251:1–12

    Google Scholar 

  42. Liu Y, Jin M, Pan S (2022) Graph self-supervised learning: A survey. IEEE Trans Knowl Data Eng 35(6):5879–5900

    Google Scholar 

  43. Liu K (2023) Multimodal graph contrastive learning for multimedia-based recommendation. IEEE Trans Multimedia 726–735

  44. Ma Y (2023) Enhancing recommendations with contrastive learning from collaborative knowledge graph. Neurocomputing 523:103–115

    Article  Google Scholar 

  45. Zou D, Wei W, Wang Z (2022) Improving knowledge-aware recommendation with multi-level interactive contrastive learning. In: Proceedings of the 31th ACM international conference on information & knowledge management, pp 2817–2826

  46. Yang Y, Huang C, Xia L (2022) Knowledge graph contrastive learning for recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 1434–1443

  47. Li Q, Ma H, Zhang R (2023) Dual-view co-contrastive learning for multi-behavior recommendation. Appl Intell 1–18

  48. Kingma D (2014) A method for stochastic optimization. Comput Sci 1–15

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Acknowledgements

The work described in this paper is partially supported by the National Natural Science Foundation of China (No. 61402150, 61806074), by Science and Technology Research Project in Henan Province (No. 232102211029), by the Key Technologies R &D Program of Henan (No. 182102410063), and by Key Scientific Research Project Plan of Colleges and Universities in Henan Province (No. 23A520016)

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Authors and Affiliations

  1. College of Computer and Information Engineering, Henan University, 85 Jinming Avenue, Kaifeng, 475004, Henan, China

    Zheng Li, Jiahao Liu, Wei Yang & Chun Liu

  2. Henan Engineering Laboratory of Spatial Information Processing, Henan University, 85 Jinming Avenue, Kaifeng, 475004, Henan, China

    Zheng Li, Wei Yang & Chun Liu

  3. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, 85 Jinming Avenue, Kaifeng, 475004, Henan, China

    Zheng Li, Wei Yang & Chun Liu

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  1. Zheng Li
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  2. Jiahao Liu
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  3. Wei Yang
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  4. Chun Liu
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Contributions

Zheng Li: Writing-review and editing, Supervision, Funding acquisition; Jiahao Liu: Conceptualization, Methodology, Software, Writing-original draft, Writing-review and editing; Wei Yang and Chun Liu: Writing-review and editing

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Correspondence to Wei Yang.

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Li, Z., Liu, J., Yang, W. et al. Joint modeling of user and item preferences with interaction frequency and attention for knowledge graph-based recommendation. Appl Intell 53, 26364–26383 (2023). https://doi.org/10.1007/s10489-023-04914-9

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